Computational imaging and intelligent specificity (Anastasio)

计算成像和智能特异性（Anastasio）

• 批准号: 10705173
• 负责人: Mark A Anastasio
• 金额: $ 18.81万
• 依托单位: UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-30 至 2027-06-20
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10705173
• 关键词: 3-Dimensional; Acceleration; Address; Automobile Driving; Biological; Biophotonics; Clinical; Collaborations; Computing Methodologies; Confocal Microscopy; Coupled; Data; Data Set; Documentation; Equilibrium; Goals; Image; Image Analysis; Image Enhancement; Imaging technology; Intelligence; Interference Microscopy; Label; Lasers; Learning; Machine Learning; Maps; Measurement; Measures; Methods; Microscopy; Modeling; Neurosciences; Optics; Output; Performance; Phase; Physics; Refractive Indices; Research; Resolution; Scanning; Semantics; Source; Source Code; Specificity; Stains; Supervision; System; Technology; Three-Dimensional Image; Three-Dimensional Imaging; Training; Translations; Width; Work; biomarker discovery; cellular imaging; computerized tools; data acquisition; deep learning; design; fluorescence imaging; image reconstruction; image translation; imaging biomarker; imaging modality; imaging science; improved; innovation; learning strategy; machine learning method; microscopic imaging; multimodality; novel; open source; reconstruction; superresolution imaging; supervised learning; technology research and development; tomography

SUMMARY In this technology research and development (TRD) project, advanced computational and machine learning methods will be developed that address a variety of needs related to image formation and image analysis in high-resolution label-free optical microscopy. Computational methods are being rapidly deployed that are changing the way that measurement data are acquired and improving the formation and analysis of microscopy images. The potential impact of such methods on the field of label-free microscopy is very high and can optimally leverage inherent endogenous contrast mechanisms in innovative and informative ways. The developed methods will serve as enabling technologies for many projects in the proposed center. The research will be informed by and jointly developed and evaluated with the TRD and driving biological projects. A general theme of this work is the integration of imaging science, physics- and deep learning (DL)-based approaches to circumvent the limitations of label-free imaging and the use of objective image quality measures to systematically validate and refine the developed methods. Three broad classes of computational methods will be investigated that will enable the (1) image-to-image mapping of label-free images to provide computational specificity, improved semantic segmentation, and/or enhanced spatial resolution; (2) improved reconstruction of images for 3D cellular imaging; and (3) extraction of biologically relevant information from multi-modality label-free image data. The Specific Aims of the project are: Aim 1: Image-to-image translation methods for providing specificity, semantic segmentation, and/or enhanced spatial resolution; Aim 2: Diffraction tomography and inverse scattering methods for 3D imaging; and Aim 3: Biomarker discovery and multi-modal DL methods. This successful completion of this project will result in computational and DL methods that will advance a variety of label-free imaging technologies. These methods will enable improved computational staining, enhance of spatial resolution, semantic segmentation, 3D image formation, and analysis of multi-modality label-free image data. They will be systematically validated for use in the biomedical applications that are within the purview of the proposed P41 center. All source code, trained models and documentation will be made open-source and shared online.

总结 在这个技术研发（TRD）项目中，先进的计算和机器学习 将开发解决与图像形成和图像分析相关的各种需求的方法， 高分辨率无标记光学显微镜。计算方法正在迅速部署， 改变测量数据的获取方式，改善显微镜的形成和分析 图像.这种方法对无标记显微镜领域的潜在影响非常大， 以创新和信息丰富的方式利用固有的内源性对比机制。发达 这些方法将成为拟建中心许多项目的技术支持。这项研究将 由TRD提供信息，与TRD共同开发和评估，并推动生物项目。一个总的主题 这项工作的核心是整合成像科学，物理学和基于深度学习（DL）的方法， 规避无标记成像的限制和使用客观的图像质量措施， 验证和完善开发的方法。将研究三大类计算方法 这将使得（1）无标记图像的图像到图像映射能够提供计算特异性， 改进的语义分割和/或增强的空间分辨率;（2）改进的图像重建， 3D细胞成像;以及（3）从多模态无标记图像中提取生物相关信息 数据该项目的具体目标是：目标1：提供特异性的图像到图像翻译方法， 语义分割和/或增强的空间分辨率;目标2：衍射层析成像和逆成像 目标3：生物标记物发现和多模态DL方法。 这个项目的成功完成将导致计算和DL方法，将推进各种 无标记成像技术的一部分。这些方法将使得能够改进计算染色，增强免疫原性。 空间分辨率、语义分割、3D图像形成和多模态无标记图像分析 数据它们将被系统地验证，以用于生物医学应用， P41中心所有源代码、经过训练的模型和文档都将开源， 分享到网上。